The goal of this work is to accelerate this process by developing an accurate, automated method for detecting fiber bundles in histological sections. Existing work on automated fiber bundle segmentation on tracer data is quite scarce, and has been done primarily in marmoset brains [1]}. In contrast with these new world primates, the cortex of old world primates, (i.e. macaques) is evolutionarily much closer to humans and, as such the trajectory of fibers are more similar [2]}, [3]}. In general, automated fiber bundle detection on anatomic tracing data is challenging due to various confounding structures (e.g., terminal fields), sectioning distortions, localized and speckle artifacts and varying background intensities/textures. In this work, we propose a deep learning-based method for fully automated, accurate fiber bundle detection for the first time on tracer data from macaque brains, using only a few manually labeled sections. We use a multi-tasking model with anatomy-constrained self-supervised loss and utilise continuity priors to ensure accurate detection and to avoid false positives (FPs). So far, various semi/self-supervised and ensembling techniques have been shown to work well on noisy data and limited labels with uncertainties [4]}, [5]}, [6]}, [7]}, [8]}. Given the shortage of manually annotated tracer data, we successfully adapt a semi-supervised training technique to improve fiber bundle detection. We also evaluate the robustness of fiber bundle detection by validating our method on unseen sections from a different macaque brain.
